As the healthcare industry shifts from fee-for-service to outcome-based contracts, providers and payors are forming new breeds of strategic partnerships. Recently, a large healthcare system was exploring a joint commercial ACO product with a national payor. In exchange for aggressive unit cost reductions, the payor promised an exclusive network that would steer new patients to the healthcare system. The payor shared its own financial model and proposed performance-based terms for an agreement. Was it a good deal? In preparing for negotiations, the healthcare system engaged Point B to develop a predictive analytic framework that would provide a clear picture of the agreement’s bottom-line impact across a range of utilization, reimbursement and enrollment scenarios.
One of our early concerns, and contributions, involved setting up the problem correctly, since that would determine the quality of all findings to come. What were the most important drivers of our client’s business? Enrollment? Utilization reduction? Unit discounts? “Steerage” of new patients as a result of the exclusive network?
Once we’d defined the most significant drivers, we developed a simulation model that would evaluate financial performance under a wide range of scenarios. Unlike traditional deterministic models, which typically include three scenarios, our stochastic model was able to quickly run a vast array of scenarios and assess the probability of their outcomes.
This analytic approach enabled our client to quickly distill a complex decision with a multitude of variables down to the most sensitive factors driving financial performance. In the process, our client gained a new understanding of the relative sensitivity of each driver to financial performance, and how it could impact negotiations with the payor.
Negotiating with new insight
Through our simulation, we showed our client that the potential financial downside of the proposed agreement was worse than originally forecast by more traditional, static analyses. Simulation depicted a financial downside that was between $5-10 million worse than originally considered in a static base case. Equipped with this analysis, our client was able to enter negotiations confidently, with a clear understanding of its interests and what it would need to be successful.
Going forward, our client plans to use this predictive analytic framework to evaluate other payor arrangements. It’s a reality check that helps ensure a win/win. After all, for the good of both parties and their patients, these new partnerships need to be fiscally smart and sustainable for everyone.